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Monoacylglycerol lipase (MAGL) is an attractive therapeutic target for many pathologies, including neurodegenerative diseases, cancer as well as chronic pain and inflammatory pathologies. The identification of reversible MAGL inhibitors, devoid of the side effects associated to prolonged MAGL inactivation, is a hot topic in medicinal chemistry. In this study, a novel phenyl(piperazin-1-yl)methanone inhibitor of MAGL was identified through a virtual screening protocol based on a fingerprint-driven consensus docking (CD) approach. Molecular modeling and preliminary structure-based hit optimization studies allowed the discovery of derivative 4, which showed an efficient reversible MAGL inhibition (IC 50 = 6.1 µM) and a promising antiproliferative activity on breast and ovarian cancer cell lines (IC 50 of 31-72 µM), thus representing a lead for the development of new and more potent reversible MAGL inhibitors. Moreover, the obtained results confirmed the reliability of the fingerprint-driven CD approach herein developed.

Monoacylglycerol lipase (MAGL) is the enzyme hydrolyzing the endocannabinoid 2-arachidonoylglycerol (2-AG) to free arachidonic acid and glycerol. Therefore, MAGL is implicated in many physiological processes involving the regulation of the endocannabinoid system and eicosanoid network. MAGL inhibition represents a potential therapeutic target for many diseases, including cancer. Nowadays, most MAGL inhibitors inhibit this enzyme by an irreversible mechanism of action, potentially leading to unwanted side effects from chronic treatment. Herein, we report the discovery of long-chain salicylketoxime derivatives as potent and reversible MAGL inhibitors. The compounds herein described are characterized by a good target selectivity for MAGL and by antiproliferative activities against a series of cancer cell lines. Finally, modeling studies suggest a reasonable hypothetical binding mode for this class of compounds., (Copyright © 2018 Elsevier Masson SAS. All rights reserved.)

We report on the virtual screening, synthesis, and biological evaluation of new furan derivatives targeting Mycobacterium tuberculosis salicylate synthase (MbtI). A receptor-based virtual screening procedure was applied to screen the Enamine database, identifying two compounds, I and III, endowed with a good enzyme inhibitory activity. Considering the most active compound I as starting point for the development of novel MbtI inhibitors, we obtained new derivatives based on the furan scaffold. Among the SAR performed on this class, compound 1a emerged as the most potent MbtI inhibitor reported to date (K i  = 5.3 μM). Moreover, compound 1a showed a promising antimycobacterial activity (MIC 99  = 156 μM), which is conceivably related to mycobactin biosynthesis inhibition., (Copyright © 2018 Elsevier Masson SAS. All rights reserved.)

Glycogen synthase kinase-3 beta (GSK3β) is a serine/threonine kinase that plays key roles in glycogen metabolism, Wnt/β-catenin signaling cascade, synaptic modulation, and multiple autophagy-related signaling pathways. GSK3β is an attractive target for drug discovery since its aberrant activity is involved in the development of neurodegenerative diseases such as Alzheimer's and Parkinson's disease. In the present study, multiple machine learning models aimed at identifying novel GSK3β inhibitors were developed and evaluated for their predictive reliability. The most powerful models were combined in a consensus approach, which was used to screen about 2 million commercial compounds. Our consensus machine learning-based virtual screening led to the identification of compounds G1 and G4, which showed inhibitory activity against GSK3β in the low-micromolar and sub-micromolar range, respectively. These results demonstrated the reliability of our virtual screening approach. Moreover, docking and molecular dynamics simulation studies were employed for predicting reliable binding modes for G1 and G4, which represent two valuable starting points for future hit-to-lead and lead optimization studies. [ABSTRACT FROM AUTHOR]

Very recently, we have developed a new generation of ligands targeting the cannabinoid receptor type 2 (CB2R), namely JR compounds, which combine the pharmacophoric portion of the CB2R positive allosteric modulator (PAM), EC21a, with that of the CB2R selective orthosteric agonist LV62, both synthesized in our laboratories. The functional examination enabled us to identify JR14a, JR22a, and JR64a as the most promising compounds of the series. In the current study, we focused on the assessment of the bitopic (dualsteric) nature of these three compounds. Experiments in cAMP assays highlighted that only JR22a behaves as a CB2R bitopic (dualsteric) ligand. In parallel, computational studies helped us to clarify the binding mode of these three compounds at CB2R, confirming the bitopic (dualsteric) nature of JR22a. Finally, the potential of JR22a to prevent neuroinflammation was investigated on a human microglial cell inflammatory model. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases (NDs) are described as multifactorial and progressive syndromes with compromised cognitive and behavioral functions. The multi-target-directed ligand (MTDL) strategy is a promising paradigm in drug discovery, potentially leading to new opportunities to manage such complex diseases. Here, we studied the dual ability of a set of resveratrol (RSV) analogs to inhibit two important targets involved in neurodegeneration. The stilbenols 1–9 were tested as inhibitors of the human monoamine oxidases (MAOs) and carbonic anhydrases (CAs). The studied compounds displayed moderate to excellent in vitro enzyme inhibitory activity against both enzymes at micromolar/nanomolar concentrations. Among them, the best compound 4 displayed potent and selective inhibition against the MAO-B isoform (IC50 MAO-A 0.43 µM vs. IC50 MAO-B 0.01 µM) with respect to the parent compound resveratrol (IC50 MAO-A 13.5 µM vs. IC50 MAO-B > 100 µM). It also demonstrated a selective inhibition activity against hCA VII (KI 0.7 µM vs. KI 4.3 µM for RSV). To evaluate the plausible binding mode of 1–9 within the two enzymes, molecular docking and dynamics studies were performed, revealing specific and significant interactions in the active sites of both targets. The new compounds are of pharmacological interest in view of their considerably reduced toxicity previously observed, their physicochemical and pharmacokinetic profiles, and their dual inhibitory ability. Compound 4 is noteworthy as a promising lead in the development of MAO and CA inhibitors with therapeutic potential in neuroprotection. [ABSTRACT FROM AUTHOR]

Inhibitors of monoamine oxidase (MAO)-B have been used for many years in the therapy of Parkinson's disease (PD). Owing to the safety concerns of the currently used agents, the discovery of novel scaffolds is of considerable interest. MAO-B inhibitory potential of rutin, a flavonoid derived from natural sources, has been established in experimental findings. Hence, the current study seeks to examine the interactions between rutin and crystal structure of human MAO-B enzyme. Molecular docking calculations, as well as molecular dynamics simulations, were employed to investigate the binding mode and the stability of the rutin/MAO-B complex. Energies of highest occupied/lowest unoccupied molecular orbitals were computed through DFT studies and used to calculate electron affinity, hardness, chemical potential, electronegativity, and electrophilicity index in order to investigate the capability of these parameters to influence the ligand–receptor interactions. It was found that rutin traverses both the entrance cavity and the substrate cavity, forcing the Ile-199 'gate' to rotate into its open conformation. It results in the fusion of the two cavities of the MAO-B binding site and directly leads to better binding interactions. Results of the current study can be used for lead modification and development of novel drugs for the treatment of PD. [ABSTRACT FROM AUTHOR]

[Display omitted] A rapid and reliable evaluation of the aqueous solubility of small molecules is a hot topic for the scientific community and represents a field of particular interest in drug discovery. In fact, aqueous solubility significantly impacts various aspects that collectively influence a drug's overall pharmacokinetics, including absorption, distribution and metabolism. For this reason, in silico approaches that provide fast and cost-effective solubility predictions, can serve as invaluable tools in the early stages of drug development. Although additional molecular features should be considered, accurate solubility predictions can help medicinal chemists rationally planning the synthesis of compounds more likely to exhibit desirable pharmacokinetic properties and in selecting the most promising candidates for further biological testing (e.g., cellular assays) from an initial pool of hit compounds with detected preliminary bioactivity. In this context, we herein report the development and evaluation of WaSPred, our AI-based water solubility predictor for small molecules. WaSPred not only showed high reliability in water solubility predictions performed on structurally heterogeneous compounds, belonging to multiple external datasets, but also demonstrated superior performance compared to a set of other commonly used water solubility predictors, thus confirming its state-of the-art robustness and its usefulness as an in silico approach for water solubility evaluations.. [ABSTRACT FROM AUTHOR]